scholarly journals Naïve Bayes classifier predicts functional microRNA target interactions in colorectal cancer

2015 ◽  
Vol 11 (8) ◽  
pp. 2126-2134 ◽  
Author(s):  
Raheleh Amirkhah ◽  
Ali Farazmand ◽  
Shailendra K. Gupta ◽  
Hamed Ahmadi ◽  
Olaf Wolkenhauer ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Mirna Target ◽  
Naive Bayes ◽  
Learning Approach ◽  
Bayes Classifier ◽  
Microrna Target ◽  
Machine Learning Approach ◽  
Specific Prediction ◽  
Novel Method

The article describes a novel method (CRCmiRTar) for a CRC-specific prediction of functional miRNA-target interactions based on a machine learning approach.

scholarly journals A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data

2015 ◽  
Vol 34 (21) ◽  
pp. 2941-2957 ◽  
Author(s):  
Julian Wolfson ◽  
Sunayan Bandyopadhyay ◽  
Mohamed Elidrisi ◽  
Gabriela Vazquez-Benitez ◽  
David M. Vock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning Approach ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Machine Learning Approach

scholarly journals Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

2021 ◽  
Vol 1 (1) ◽  
pp. 57-70
Author(s):  
Ranjan Raj Aryal ◽  
Ankit Bhattarai
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Other ◽  
Learning Approach ◽  
Asian Countries ◽  
Vaccination Programs ◽  
Machine Learning Approach ◽  
Positive Sentiment

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

scholarly journals Managing Churn to Maximize Profits

2020 ◽  
Vol 39 (5) ◽  
pp. 956-973 ◽  
Author(s):  
Aurélie Lemmens ◽  
Sunil Gupta
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Novel Method

This article provides a novel method to optimize the profits of proactive retention campaigns using a machine learning approach to causal inference.

scholarly journals Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

2018 ◽  
Author(s):  
Nathan Wan ◽  
David Weinberg ◽  
Tzu-Yu Liu ◽  
Katherine Niehaus ◽  
Daniel Delubac ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cross Validation ◽  
Early Stage ◽  
Learning Approach ◽  
Whole Genome ◽  
Free Dna ◽  
Machine Learning Approach

AbstractBackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.MethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.ResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.ConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

Sign in / Sign up

Export Citation Format

Share Document